223 citations · 475 across the 51 of their papers we have counts for
6 papers · 1 filter
Optical Flow Matters: an Empirical Comparative Study on Fusing Monocular Extracted Modalities for Better Steering
Fouad Makiyeh, Mark Bastourous, Anass Bairouk +7
Autonomous vehicle navigation is a key challenge in artificial intelligence, requiring robust and accurate decision-making processes. This research introduces a new end-to-end meth…
Human Insights Driven Latent Space for Different Driving Perspectives: A Unified Encoder for Efficient Multi-Task Inference
Huy-Dung Nguyen, Anass Bairouk, Mirjana Maras +6
Autonomous driving systems require a comprehensive understanding of the environment, achieved by extracting visual features essential for perception, planning, and control. However…
State-Free Inference of State-Space Models: The Transfer Function Approach
Rom N. Parnichkun, Stefano Massaroli, Alessandro Moro +10
We approach designing a state-space model for deep learning applications through its dual representation, the transfer function, and uncover a highly efficient sequence parallel in…
Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks
Alex Quach, Makram Chahine, Alexander Amini +2
Simulators are powerful tools for autonomous robot learning as they offer scalable data generation, flexible design, and optimization of trajectories. However, transferring behavio…
Exploring Latent Pathways: Enhancing the Interpretability of Autonomous Driving with a Variational Autoencoder
Anass Bairouk, Mirjana Maras, Simon Herlin +5
Autonomous driving presents a complex challenge, which is usually addressed with artificial intelligence models that are end-to-end or modular in nature. Within the landscape of mo…
Learning with Chemical versus Electrical Synapses -- Does it Make a Difference?
Mónika Farsang, Mathias Lechner, David Lung +3
Bio-inspired neural networks have the potential to advance our understanding of neural computation and improve the state-of-the-art of AI systems. Bio-electrical synapses directly…